SoroushVahidi/scidocs huggingface dataset:
A task description language for robot control
(1998)

Robot systems must achieve high level goals while remaining reactive to contingencies and new opportunities. This typically requires robot systems to coordinate concurrent activities, monitor the environment, and deal with exceptions. We have developed a new language to support such task-level control. The language, TDL, is an extension of C++ that provides syntactic support for task decomposition, synchronization, execution monitoring, and exception handling. A compiler transforms TDL into pure C++ code that utilizes a platform-independent task management library. This paper introduces TDL, describes the task treerepresentation that underlies the language, and presents some aspects of its implementation and use in an autonomous mobile robot.
Contrast masking in human vision.
(1980)

Contrast masking was studied psychophysically. A two-alternative forced-choice procedure was used to measure contrast thresholds for 2.0 cpd sine-wave gratings in the presence of masking sine-wave gratings. Thresholds were measured for 11 masker contrasts spanning three log units, and seven masker frequencies ranging +/- one octave from the signal frequency. Corresponding measurements were made for gratings with horizontal widths of 0.75 degrees (narrow fields) and 6.0 degrees (wide fields). For high contrast maskers at all frequencies, signal thresholds were related to masking contrast by power functions with exponents near 0.6. For a range of low masking contrasts, signal thresholds were reduced by the masker. For the wide fields, high contrast masking tuning functions peaked at the signal frequency, were slightly asymmetric, and had approximately invariant half-maximum frequencies that lie 3/4 octave below and 1 octave above the signal frequency. The corresponding low contrast tuning functions exhibited peak threshold reduction at the signal frequency, with half-minimum frequencies at roughly +/- 0.25 octaves. For the narrow fields, the masking tuning functions were much broader at both low and high masking contrasts. A masking model is presented that encompasses contrast detection, discrimination, and masking phenomena. Central constructs of the model include a linear spatial frequency filter, a nonlinear transducer, and a process of spatial pooling that acts at low contrasts only.
Stages of embryonic development of the zebrafish.
(1995)

We describe a series of stages for development of the embryo of the zebrafish, Danio (Brachydanio) rerio. We define seven broad periods of embryogenesis--the zygote, cleavage, blastula, gastrula, segmentation, pharyngula, and hatching periods. These divisions highlight the changing spectrum of major developmental processes that occur during the first 3 days after fertilization, and we review some of what is known about morphogenesis and other significant events that occur during each of the periods. Stages subdivide the periods. Stages are named, not numbered as in most other series, providing for flexibility and continued evolution of the staging series as we learn more about development in this species. The stages, and their names, are based on morphological features, generally readily identified by examination of the live embryo with the dissecting stereomicroscope. The descriptions also fully utilize the optical transparancy of the live embryo, which provides for visibility of even very deep structures when the embryo is examined with the compound microscope and Nomarski interference contrast illumination. Photomicrographs and composite camera lucida line drawings characterize the stages pictorially. Other figures chart the development of distinctive characters used as staging aid signposts.
A^2-Nets: Double Attention Networks
(2018)

Learning to capture long-range relations is fundamental to image/video recognition. Existing CNN models generally rely on increasing depth to model such relations which is highly inefficient. In this work, we propose the “double attention block”, a novel component that aggregates and propagates informative global features from the entire spatio-temporal space of input images/videos, enabling subsequent convolution layers to access features from the entire space efficiently. The component is designed with a double attention mechanism in two steps, where the first step gathers features from the entire space into a compact set through second-order attention pooling and the second step adaptively selects and distributes features to each location via another attention. The proposed double attention block is easy to adopt and can be plugged into existing deep neural networks conveniently. We conduct extensive ablation studies and experiments on both image and video recognition tasks for evaluating its performance. On the image recognition task, a ResNet-50 equipped with our double attention blocks outperforms a much larger ResNet-152 architecture on ImageNet-1k dataset with over 40% less the number of parameters and less FLOPs. On the action recognition task, our proposed model achieves the state-of-the-art results on the Kinetics and UCF-101 datasets with significantly higher efficiency than recent works.
Automating hierarchical document classification for construction management information systems
(2003)

The widespread use of information technologies for construction is considerably increasing the number of electronic text documents stored in construction management information systems. Consequently, automated methods for organizing and improving the access to the information contained in these types of documents become essential to construction information management. This paper describes a methodology developed to improve information organization and access in construction management information systems based on automatic hierarchical classification of construction project documents according to project components. A prototype system for document classification is presented, as well as the experiments conducted to verify the feasibility of the proposed approach. D 2003 Elsevier Science B.V. All rights reserved.
A SeqGAN for Polyphonic Music Generation
(2017)

We propose an application of SeqGAN, generative adversarial networks for discrete sequence generation, for creating polyphonic musical sequences. Instead of monophonic melody generation suggested in the original work, we present an efficient representation of polyphony MIDI file that captures chords and melodies with dynamic timings simultaneously. The network can create sequences that are musically coherent. We also report that careful tuning of reinforcement learning signals of the model are crucial for general application.
Multilinear Discriminant Analysis for Higher-Order Tensor Data Classification
(2014)

In the past decade, great efforts have been made to extend linear discriminant analysis for higher-order data classification, generally referred to as multilinear discriminant analysis (MDA). Existing examples include general tensor discriminant analysis (GTDA) and discriminant analysis with tensor representation (DATER). Both the two methods attempt to resolve the problem of tensor mode dependency by iterative approximation. GTDA is known to be the first MDA method that converges over iterations. However, its performance relies highly on the tuning of the parameter in the scatter difference criterion. Although DATER usually results in better classification performance, it does not converge, yet the number of iterations executed has a direct impact on DATER's performance. In this paper, we propose a closed-form solution to the scatter difference objective in GTDA, namely, direct GTDA (DGTDA) which also gets rid of parameter tuning. We demonstrate that DGTDA outperforms GTDA in terms of both efficiency and accuracy. In addition, we propose constrained multilinear discriminant analysis (CMDA) that learns the optimal tensor subspace by iteratively maximizing the scatter ratio criterion. We prove both theoretically and experimentally that the value of the scatter ratio criterion in CMDA approaches its extreme value, if it exists, with bounded error, leading to superior and more stable performance in comparison to DATER.
Blind grasping: Stable robotic grasping using tactile feedback and hand kinematics
(2011)

We propose a machine learning approach to the perception of a stable robotic grasp based on tactile feedback and hand kinematic data, which we call blind grasping. We first discuss a method for simulating tactile feedback using a soft finger contact model in GraspIt!, which is a robotic grasping simulator [10]. Using this simulation technique, we compute tactile contacts of thousands of grasps with a robotic hand using the Columbia Grasp Database [6]. The tactile contacts along with the hand kinematic data are then input to a Support Vector Machine (SVM) which is trained to estimate the stability of a given grasp based on this tactile feedback and also the robotic hand kinematics. Experimental results indicate that the tactile feedback along with the hand kinematic data carry meaningful information for the prediction of the stability of a blind robotic grasp.
Cortical dynamics of contextually cued attentive visual learning and search: spatial and object evidence accumulation.
(2010)

How do humans use target-predictive contextual information to facilitate visual search? How are consistently paired scenic objects and positions learned and used to more efficiently guide search in familiar scenes? For example, humans can learn that a certain combination of objects may define a context for a kitchen and trigger a more efficient search for a typical object, such as a sink, in that context. The ARTSCENE Search model is developed to illustrate the neural mechanisms of such memory-based context learning and guidance and to explain challenging behavioral data on positive-negative, spatial-object, and local-distant cueing effects during visual search, as well as related neuroanatomical, neurophysiological, and neuroimaging data. The model proposes how global scene layout at a first glance rapidly forms a hypothesis about the target location. This hypothesis is then incrementally refined as a scene is scanned with saccadic eye movements. The model simulates the interactive dynamics of object and spatial contextual cueing and attention in the cortical What and Where streams starting from early visual areas through medial temporal lobe to prefrontal cortex. After learning, model dorsolateral prefrontal cortex (area 46) primes possible target locations in posterior parietal cortex based on goal-modulated percepts of spatial scene gist that are represented in parahippocampal cortex. Model ventral prefrontal cortex (area 47/12) primes possible target identities in inferior temporal cortex based on the history of viewed objects represented in perirhinal cortex.
Improving bug triage with bug tossing graphs
(2009)

bug report is typically assigned to a single developer who is then responsible for fixing the bug. In Mozilla and Eclipse, between 37%-44% of bug reports are "tossed" (reassigned) to other developers, for example because the bug has been assigned by accident or another developer with additional expertise is needed. In any case, tossing increases the time-to-correction for a bug.
 In this paper, we introduce a graph model based on Markov chains, which captures bug tossing history. This model has several desirable qualities. First, it reveals developer networks which can be used to discover team structures and to find suitable experts for a new task. Second, it helps to better assign developers to bug reports. In our experiments with 445,000 bug reports, our model reduced tossing events, by up to 72%. In addition, the model increased the prediction accuracy by up to 23 percentage points compared to traditional bug triaging approaches.
Where should the bugs be fixed? More accurate information retrieval-based bug localization based on bug reports
(2012)

For a large and evolving software system, the project team could receive a large number of bug reports. Locating the source code files that need to be changed in order to fix the bugs is a challenging task. Once a bug report is received, it is desirable to automatically point out to the files that developers should change in order to fix the bug. In this paper, we propose BugLocator, an information retrieval based method for locating the relevant files for fixing a bug. BugLocator ranks all files based on the textual similarity between the initial bug report and the source code using a revised Vector Space Model (rVSM), taking into consideration information about similar bugs that have been fixed before. We perform large-scale experiments on four open source projects to localize more than 3,000 bugs. The results show that BugLocator can effectively locate the files where the bugs should be fixed. For example, relevant buggy files for 62.60% Eclipse 3.1 bugs are ranked in the top ten among 12,863 files. Our experiments also show that BugLocator outperforms existing state-of-the-art bug localization methods.
Automatic Bug Triage using Semi-Supervised Text Classification
(2010)

In this paper, we propose a semi-supervised text classification approach for bug triage to avoid the deficiency of labeled bug reports in existing supervised approaches. This new approach combines naive Bayes classifier and expectationmaximization to take advantage of both labeled and unlabeled bug reports. This approach trains a classifier with a fraction of labeled bug reports. Then the approach iteratively labels numerous unlabeled bug reports and trains a new classifier with labels of all the bug reports. We also employ a weighted recommendation list to boost the performance by imposing the weights of multiple developers in training the classifier. Experimental results on bug reports of Eclipse show that our new approach outperforms existing supervised approaches in terms of classification accuracy. Keywordsautomatic bug triage; expectation-maximization; semi-supervised text classification; weighted recommendation list
DREX: Developer Recommendation with K-Nearest-Neighbor Search and Expertise Ranking
(2011)

This paper proposes a new approach called DREX (Developer Recommendation with k-nearest-neighbor search and Expertise ranking) to developer recommendation for bug resolution based on K-Nearest-Neighbor search with bug similarity and expertise ranking with various metrics, including simple frequency and social network metrics. We collect Mozilla Fire fox open bug repository as the experimental data set and compare different ranking metrics on the performance of recommending capable developers for bugs. Our experimental results demonstrate that, when recommending 10 developers for each one of the 250 testing bugs, DREX has produced better performance than traditional methods with multi-labeled text categorization. The best performance obtained by two metrics as Out-Degree and Frequency, is with recall as 0.6 on average. Moreover, other social network metrics such as Degree and Page Rank have produced comparable performance on developer recommendation as Frequency when used for developer expertise ranking.
Assigning bug reports using a vocabulary-based expertise model of developers
(2009)

For popular software systems, the number of daily submitted bug reports is high. Triaging these incoming reports is a time consuming task. Part of the bug triage is the assignment of a report to a developer with the appropriate expertise. In this paper, we present an approach to automatically suggest developers who have the appropriate expertise for handling a bug report. We model developer expertise using the vocabulary found in their source code contributions and compare this vocabulary to the vocabulary of bug reports. We evaluate our approach by comparing the suggested experts to the persons who eventually worked on the bug. Using eight years of Eclipse development as a case study, we achieve 33.6% top-1 precision and 71.0% top-10 recall.
Understanding the determinants of business intelligence system adoption stages: An empirical study of SMEs
(2018)

Although business intelligence systems (BIS) adoption research has progressed considerably since its early inceptions, our understanding of the influence of BIS determinants across different adoption stages remains limited. In response, we develop and empirically test a conceptual model for assessing the determinants of BIS diffusion on the evaluation, adoption, and use stages. The model is based on two prominent, firm-level adoption concepts: the diffusion of innovation (DOI) and the technology, organization, environment (TOE) framework, extended with our previous research findings. Drawing on data from 181 small and medium enterprises (SMEs), we identify seven distinct determinants (i.e. cost, BIS is part of ERP, management support, rational decision-making culture, project champion, organizational data environment, organizational readiness) as statistically significant for different adoption stages.
The Stratosphere platform for big data analytics
(2014)

We present Stratosphere, an open-source software stack for parallel data analysis. Stratosphere brings together a unique set of features that allow the expressive, easy, and efficient programming of analytical applications at very large scale. Stratosphere’s features include “in situ” data processing, a declarative query language, treatment of user-defined functions as first-class citizens, automatic program parallelization and optimization, support for iterative programs, and a scalable and efficient execution engine. Stratosphere covers a variety of “Big Data” use cases, such as data warehousing, information extraction and integration, data cleansing, graph analysis, and statistical analysis applications. In this paper, we present the overall system architecture design decisions, introduce Stratosphere through example queries, and then dive into the internal workings of the system’s components that relate to extensibility, programming model, optimization, and query execution. We experimentally compare Stratosphere against popular open-source alternatives, and we conclude with a research outlook for the next years.
Improving Duckworth lewis method by using machine learning
(2017)

There has been lot of research has been done in the area of sports with prediction. Machine learning algorithm has been applied on most of the data to predict its outcome. In cricket also there are many research has been done to find out the outcome of the match. But there are a special case in the cricket where statistic plays an important role. When the cricket match is interrupted by the rain and then again it starts during the shortage of the time it is difficult to predict the run and overs remaining in the cricket match.To over come this issue Duck worth lewis method has been introduced. In this paper we are going to discuss the short come of the method of Duck worth lewis method. In this research We are going to improve the model of duck worth lewis method with machine learning models. At end we are going to compare the Duck worth lewis method, Duck worth lewis method with run rate as a variable and Improved version of duck worth lewis method.
Push-Net: Deep Planar Pushing for Objects with Unknown Physical Properties
(2018)

This paper introduces Push-Net, a deep recurrent neural network model, which enables a robot to push objects of unknown physical properties for re-positioning and re-orientation, using only visual camera images as input. The unknown physical properties is a major challenge for pushing. Push-Net overcomes the challenge by tracking a history of push interactions with an LSTM module and training an auxiliary objective function that estimates an object’s center of mass. We trained Push-Net entirely in simulation and tested it extensively on many different objects in both simulation and on two real robots, a Fetch arm and a Kinova MICO arm. Experiments suggest that Push-Net is robust and efficient. It achieved over 97% success rate in simulation on average and succeeded in all real robot experiments with a small number of pushes.
Neural Task Programming: Learning to Generalize Across Hierarchical Tasks
(2018)

In this work, we propose a novel robot learning framework called Neural Task Programming (NTP), which bridges the idea of few-shot learning from demonstration and neural program induction. NTP takes as input a task specification (e.g., video demonstration of a task) and recursively decomposes it into finer sub-task specifications. These specifications are fed to a hierarchical neural program, where bottom-level programs are callable subroutines that interact with the environment. We validate our method in three robot manipulation tasks. NTP achieves strong generalization across sequential tasks that exhibit hierarchal and compositional structures. The experimental results show that NTP learns to generalize well towards unseen tasks with increasing lengths, variable topologies, and changing objectives.stanfordvl.github.io/ntp/.
Functional reactive programming from first principles
(2000)

Functional Reactive Programming, or FRP, is a general framework for programming hybrid systems in a high-level, declarative manner. The key ideas in FRP are its notions of behaviors and events. Behaviors are time-varying, reactive values, while events are time-ordered sequences of discrete-time event occurrences. FRP is the essence of Fran, a domain-specific language embedded in Haskell for programming reactive animations, but FRP is now also being used in vision, robotics and other control systems applications. 
In this paper we explore the formal semantics of FRP and how itrelates to an implementation based on streams that represent (and therefore only approximate) continuous behaviors. We show that, in the limit as the sampling interval goes to zero, the implementation is faithful to the formal, continuous semantics, but only when certain constraints on behaviors are observed. We explore the nature of these constraints, which vary amongst the FRP primitives. Our results show both the power and limitations of this approach to language design and implementation. As an example of a limitation, we show that streams are incapable of representing instantaneous predicate events over behaviors.
A Wide-band Circular Polarization Stacked Patch Antenna for the Wireless Communication Applications
(2008)

A wide-band ‘corners-truncated rectangular’ stacked patch antenna for use in the circular polarization applications was proposed. The antenna proposed in this paper an axial ratio of less than 3 dB and a VSWR of less than 2 : 1 were shown to be achievable over a 25% bandwidth for use in the wireless communication applications, and this antenna can achieves higher gain, lower side lobes and wider bandwidth compared to the traditional microstrip patch antenna.
An upper limb exoskeleton with an optimized 4R spherical wrist mechanism for the shoulder joint
(2014)

This paper presents an upper limb rehabilitation exoskeleton with an optimized 4R spherical wrist mechanism for the shoulder joint. Traditional designs of shoulder exoskeletons use 3R mechanisms to replicate the spherical motion of the shoulder. However, due to the exceptionally large range of motion of the human shoulder, the 3R mechanism is required to operate at a singular configuration at some point in the shoulder workspace. In this configuration, the 3R mechanism loses the ability to rotate the shoulder about one DOF. To overcome this problem, the use of a kinematically redundant 4R mechanism is proposed. The 4R mechanism has been optimized in a previous work and is used in the design of the presented exoskeleton prototype. This new shoulder mechanism allows the exoskeleton to achieve the entire human shoulder workspace without mechanical interference and while operating well away from singular configurations. Numerous features have also been included in the exoskeleton design to ensure it is safe, comfortable and easy to use. The shoulder mechanism of the exoskeleton is analyzed to demonstrate the capabilities of the 4R mechanism.